Bayesian Econometrics: Conjugate Analysis and Rejection Sampling - Ley, Steel - 1993Ley, E., and M. F. J. Steel. (1992). “Bayesian Econometrics: Conjugate Analysis and Rejection Sampling Using Mathematica.” In
Journal of the American Statistical AssociationTreder, R. P. and Sedransk, J., "Bayesian Sequential Two- Phase Sampling," J. Am. Stat. Assoc., Vol. 91, No. 434, 1996, pp. 782-790, https://doi.org/10.1080/01621459.1996. 10476946...
A survey of sampling-based Bayesian analysis of financial dataEVENT STUDIESINFERENCEBAYESIANMARKOV CHAIN MONTE CARLOGIBBS SAMPLERThe capability of implementing a complete Bayesian analysis of experimental data has emerged over recent years due to computational advances developed within the statistical community...
The likelihood function can be used to obtain estimates of the parameters of interest based upon the collected data using either maximum likelihood or Bayesian methods of inference. 3.1.2 Expected Values and Variance Before we can consider statistical properties of estimators we need to define ...
The Bayesian analysis of outliers in a linear model is considered. Verdinelli and Wasserman (1991) showed that an adaptive Monte Carlo integration technique known as the Gibbs sampler is a conceptually and computationally simple method for calculating posterior marginals in outlier problems. The ...
Ye, editors, Frontiers of Statistical Decision Making and Bayesian Analysis. Springer-Verlag, New York, 2007b. To appear, see arXiv:0910.2325.Marin, J.-M. and Robert, C. P. (2009), "Importance sampling methods for Bayesian dis- crimination between embedded models," arXiv preprint arXiv:...
In this paper, we study non-Bayesian and Bayesian estimation of parameters for the Kumaraswamy distribution based on progressive Type-II censoring. First, the maximum likelihood estimates and maximum product spacings are derived. In addition, we derive t
Fenton N, Neil M (2013) Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press, Boca Raton FL; 503pp. Hanea AM, Kurowicka D, Cooke RM (2006) Hybrid method for quantifying and analyzing Bayesian Belief Nets. Qual Rel Engng Int 22:709–729. https://doi.org/10.1002/qre...
This paper introduces a framework for speeding up Bayesian inference conducted in presence of large datasets. We design a Markov chain whose transition ker
Exploratory data analysisWeighted bootstrapBayesian statistics can be hard to teach at an elementary level due to the difficulty in deriving the posterior distribution for interesting nonconjugate problems. One attractive method of summarizing the posterior distribution is to directly simulate from the ...